One of the motivations for Salmon's (1984) causal theory of explanation was the explanatory irrelevance exhibited by many arguments conforming to Hempel's covering-law models of explanation. However, the nexus of causal processes and interactions characterized by Salmon is not rich enough to supply the necessary conception of explanatory relevance. Salmon's (1994) revised theory, which is briefly criticized on independent grounds, fares no better. There is some possibility that the two-tiered structure of explanation described by Salmon (1984) may be pressed into (...) service, but more work would have to be done. Ironically, Salmon's difficulties are similar to those suffered by his seventeenth-century predecessors. (shrink)

Causation is a topic of perennial philosophical concern. As well as being of intrinsic interest, almost all philosophical concepts — such as knowledge, beauty, and moral responsibility — involve a causal dimension. Nonetheless, attempts to provide a satisfactory account of the nature of causation have typically led to barrages of counterexamples. I hope to show that a number of the difficulties plaguing theories of causation have a common source.Most philosophical theories of causation describe a binary relation between cause and effect, (...) or at any rate, a relation that reduces to such a binary relation when certain background information is held fixed. Indeed, most theories provide the same general account of when this relation holds: in order to evaluate whether C causes E, we must make a comparison between two cases, which we may neutrally label as C and ∼C. Where theories of causation differ, of course, is in precisely what is being so compared. Regularity theories of causation require a comparison between what actually happens whenever C occurs, and what actually happens, elsewhere and elsewhen, when C does not occur. (shrink)

Salmon has argued that science provides explanations by describing a causal nexus: For Salmon, this nexus is a network of processes and interactions. I argue that this picture of the causal nexus is insufficient for an account of scientific explanation: a taxonomy of causal relevance is also needed. ;Probabilistic theories of causation seem to provide such a taxonomy in their dichotomy between promoting and inhibiting causes. However, standard probabilistic theories are beset by a difficulty called the problem of disjunctive factors. (...) According to such theories, an effect is more probable in the presence of a cause than in its absence . But there are many ways in which a particular cause may come about, and many in which it may be absent; these ways need not confer the same probabilities upon the effect. Thus, there is no direct comparison of two probabilities to be made. My solution is to abandon the oversimplified dichotomy between promoting and inhibiting causes. Causal claims convey qualitative information about more complex probabilistic relationships. Interestingly, this picture of causation meshes naturally with extant theories of explanation. ;I show how this framework may be employed in resolving several outstanding problems in the theories of causation and explanation, such as the relationship between singular and general causal claims, and the role of contrastive stress in causal and explanatory claims. ;Finally, I show how my account of causal explanation can be connected with the semantic conception of scientific theories. According to this conception, a theory is characterized in terms of a class of mathematical models. I describe the relationship between these mathematical models and the probabilistic relationships that are described by causal explanations. These ideas are illustrated in an extended discussion of causation and explanation in evolutionary theory. (shrink)

Generally speaking, scientific explanation has been a topic of lively discussion in twentieth‐century philosophy of science; philosophers of science have endeavored to characterize rigorously a number of different types of explanation to be found in the various fields of scientific research. Given the indispensability of statistical concepts and techniques in virtually every branch of modern science, it is natural to ask whether some scientific explanations are essentially statistical or probabilistic in character. The answer would seem to be yes. For example, (...) we can explain why atoms of carbon 14 have a 1/4 probability of surviving for 11,460 years because the half‐life of that species is 5,730 years. As we shall see, explanations of this type are not especially problematic. As another example, we might explain why a particular weed withered by citing the fact that it received a dose of a herbicide, even though we know that the herbicide is not invariably effective. This means that the withering is related probabilistically to the herbicide treatment but is not necessitated by it. Explanations of this kind, by contrast, lead to severe difficulties. (shrink)

Philosophers have used the probabilistic relation of ’screening-off‘ to explicate concepts in the theories of causation and explanation. Brandon has used screening-off relations in an attempt to reconstruct an argument of Mayr and Gould that natural selection acts at the level of the organism. I argue that Brandon‘s reconstruction is unsuccessful.